Article
Biology
P. R. Rosenbaum, D. B. Rubin
Summary: This article emphasizes the importance of design activities prior to examining outcome variables in experimental or observational studies. Balancing the propensity scores is an aspect of the design of observational studies, which can be achieved through matching or balancing on the propensity score. Controlling for observed covariates is a crucial step from association to causation.
Article
Oncology
Debashis Ghosh, Arya Amini, Bernard L. Jones, Sana D. Karam
Summary: The exclusion of unmatched observations in propensity score matching affects the generalizability of causal effects. Machine learning methods can help identify the differences between the study population and the unmatched subpopulation.
FRONTIERS IN ONCOLOGY
(2022)
Article
Health Care Sciences & Services
Stephen P. Fortin, Stephen S. Johnston, Martijn J. Schuemie
Summary: The study compared large-scale CM (LS-CM) and large-scale PSM (LS-PSM) in terms of post-match sample size, covariate balance, and residual confounding. Results showed that LS-CM could find the largest matched sample and achieve covariate balance in small sample studies.
BMC MEDICAL RESEARCH METHODOLOGY
(2021)
Article
Public, Environmental & Occupational Health
Koichiro Shiba, Takuya Kawahara
Summary: The article provides an overview of the use of propensity score methods in causal inference, comparing the matching and inverse probability weighting methods and discussing common pitfalls and application tips. Furthermore, the article compares traditional multivariable outcome regression with the two alternative propensity score-based methods, highlighting differences in statistical modeling assumptions and the target population for estimating exposure effects.
JOURNAL OF EPIDEMIOLOGY
(2021)
Article
Substance Abuse
Gary C. K. Chan, Carmen Lim, Tianze Sun, Daniel Stjepanovic, Jason Connor, Wayne Hall, Janni Leung
Summary: This paper introduces the potential outcomes framework for causal inference and summarizes well-established causal analysis methods for observational data in addiction research. It provides examples and analysis codes to assist researchers in conducting these analyses.
Article
Psychology, Applied
Kaori Narita, J. D. Tena, Claudio Detotto
Summary: Propensity score analysis is a useful method for making causal claims, but it is rarely used in leadership and applied psychology research. This paper aims to explain and discuss the application and key assumptions of the method, specifically propensity score weighting. It also examines how propensity score analysis has been conducted in recent management studies and presents an advanced application of the approach using data from Italian football.
LEADERSHIP QUARTERLY
(2023)
Article
Food Science & Technology
Shu-Chuan Kuo, Yih-Ming Weng
Summary: The study in Taiwan evaluated the effects of a food safety course on sixth graders' knowledge, attitude, and practice, compared to fifth graders. Results showed improvements in sixth graders' knowledge about food poisoning and additives, changes in attitude towards additives, and better food labeling awareness. However, some students still held misconceptions, such as believing food with additives is not good food.
Article
Mathematical & Computational Biology
Marie Salditt, Steffen Nestler
Summary: There has been limited research on nonparametric propensity score estimation in clustered data settings. This article extends existing research by proposing a general algorithm for incorporating random effects into a machine learning model for clustered IPW. The results showed that nonparametric approaches performed well in the absence of unmeasured confounding, and fixed and random effects models reduced bias compared to single-level models in marginal IPW.
STATISTICS IN MEDICINE
(2023)
Article
Environmental Sciences
Seung Min Chung
Summary: This study examined the impact of blood cadmium levels on body composition using a propensity score-matched cohort. It found that high blood cadmium levels increased the risk of body composition deterioration, especially in individuals aged 60-69 years, women, and those with adiposity obesity at baseline.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Immunology
Cheng-Long Ge, Li-Na Zhang, Yu-Hang Ai, Wei Chen, Zhi-Wen Ye, Yu Zou, Qian-Yi Peng
Summary: This study aimed to evaluate the association between b-blocker therapy and mortality in patients with sepsis. The findings showed that long-acting b-blockers were associated with improved 28- and 90-day mortality in patients with sepsis, while short-acting b-blockers did not reduce mortality in sepsis.
FRONTIERS IN CELLULAR AND INFECTION MICROBIOLOGY
(2023)
Review
Mathematical & Computational Biology
Ting-Hsuan Chang, Elizabeth A. Stuart
Summary: Propensity score methods are commonly used to estimate causal effects in observational studies, but their application in clustered data is relatively new. This article presents a framework for estimating causal effects using propensity scores when study units are nested within clusters and are nonrandomly assigned to treatment conditions.
STATISTICS IN MEDICINE
(2022)
Article
Mathematics, Interdisciplinary Applications
Zihan Lin, Ai Ni, Bo Lu
Summary: This article advocates the use of restricted mean survival time difference as a marginal causal effect measure to investigate the causal relationship between exposure and time-to-event outcome. A matched design with sensitivity analysis is proposed to address both measured and unmeasured confounding. Simulation studies and application to real data demonstrate the effectiveness of the proposed method.
JOURNAL OF CAUSAL INFERENCE
(2023)
Article
Mathematical & Computational Biology
Ting-Hsuan Chang, Trang Quynh Nguyen, Youjin Lee, John W. Jackson, Elizabeth A. Stuart
Summary: This study examines the performance of nonparametric propensity score estimation methods in settings with clustering of individuals and unmeasured cluster-level confounding. The results suggest that nonparametric methods may provide better results when the sample and cluster sizes are large, but they may be more vulnerable to unmeasured cluster-level confounding when the sizes are small, making multilevel logistic regression a potentially better alternative.
STATISTICS IN MEDICINE
(2022)
Article
Mathematical & Computational Biology
Ziwen Wang, Chenguang Wang, Xiaoguang Wang
Summary: In this paper, a causal effect estimation approach is proposed for observational studies with survival data and a cure fraction. The absolute treatment effects on survival outcomes are extended to survival outcomes with cure fractions, and the corresponding causal effect estimators are constructed based on propensity score stratification. The asymptotic properties of the proposed estimators are proven, and simulation studies are conducted to evaluate their performances. An illustration is provided using a stomach cancer study.
BIOMETRICAL JOURNAL
(2023)
Article
Biochemical Research Methods
Xinyi Xu, Xiaokang Yu, Gang Hu, Kui Wang, Jingxiao Zhang, Xiangjie Li
Summary: The proposed scPSM method utilizes propensity score matching to simultaneously correct batch effects, impute dropouts, and denoise scRNA-seq data, showing superior performance compared to other state-of-the-art methods. It improves clustering accuracy, mixes cells of the same type, and preserves real biological structure in downstream gene-based analyses.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Endocrinology & Metabolism
Yonas T. Berhan, Mats Eliasson, Anna Mollsten, Ingeborg Waernbaum, Gisela Dahlquist
Article
Public, Environmental & Occupational Health
Ingeborg Waernbaum, Gisela Dahlquist
EUROPEAN JOURNAL OF EPIDEMIOLOGY
(2016)
Article
Mathematical & Computational Biology
Emma Persson, Ingeborg Waernbaum, Torbjorn Lind
STATISTICS IN MEDICINE
(2017)
Article
Endocrinology & Metabolism
Cecilia Toppe, Anna Mollsten, Ingeborg Waernbaum, Staffan Schon, Soffia Gudbjornsdottir, Mona Landin-Olsson, Gisela Dahlquist
Article
Endocrinology & Metabolism
Ingeborg Waernbaum, Gisela Dahlquist, Torbjorn Lind
Article
Mathematical & Computational Biology
Els Goetghebeur, Saskia le Cessie, Bianca De Stavola, Erica E. M. Moodie, Ingeborg Waernbaum
STATISTICS IN MEDICINE
(2020)
Article
Medicine, General & Internal
Erik Hedstrom, Sead Crnalic, Antonia Kullstrom, Ingeborg Waernbaum
Summary: The study found that children from higher-income families with more siblings are at greater risk of sustaining fractures.
Article
Immunology
Anna M. Eikenboom, Saskia Le Cessie, Ingeborg Waernbaum, Rolf H. H. Groenwold, Mark G. J. de Boer
Summary: Propensity score methods are increasingly used in infectious disease medicine to study the effectiveness of antimicrobial therapy. However, the quality and reporting of these methods need improvement.
OPEN FORUM INFECTIOUS DISEASES
(2022)
Article
Mathematical & Computational Biology
Camila Olarte Parra, Ingeborg Waernbaum, Staffan Schon, Els Goetghebeur
Summary: This article examines the effect of pre-emptive kidney transplantation versus initial dialysis followed by delayed transplantation on all-cause mortality in end-stage renal disease cases. The study reveals that informative censoring and other missing data patterns can lead to biased treatment associations and misrepresented survival chances. The authors discuss ways to address these challenges and highlight the importance of accounting for baseline covariates.
STATISTICS IN MEDICINE
(2022)
Article
Mathematical & Computational Biology
Ingeborg Waernbaum, Laura Pazzagli
Summary: This paper proposes a crude analytical approach to study large-sample biases of estimators when the models are approximations of the data-generating process. Through the application and comparison of commonly used estimators, the results show that normalization can reduce biases caused by model misspecification when adjusting for confounding.
BIOMETRICAL JOURNAL
(2023)
Article
Endocrinology & Metabolism
Ingeborg Waernbaum, Torbjorn Lind, Anna Mollsten, Gisela Dahlquist
Summary: The incidence of childhood-onset type 1 diabetes in Sweden has reached a stable but high level in the past two decades, with a plateau between 2005 and 2019. Increased migration from countries with lower incidence rates cannot fully explain this leveling off.
Meeting Abstract
Endocrinology & Metabolism
I. Waernbaum, T. Lind, A. Mollsten, G. Dahlquist
Article
Public, Environmental & Occupational Health
Laura Pazzagli, Anna Mollsten, Ingeborg Waernbaum
ANNALS OF EPIDEMIOLOGY
(2017)
Article
Statistics & Probability
Ronnie Pingel, Ingeborg Waernbaum
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
(2017)
Article
Computer Science, Interdisciplinary Applications
Emma Persson, Jenny Haggstrom, Ingeborg Waernbaum, Xavier de Luna
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2017)
Article
Statistics & Probability
Omidali Aghababaei Jazi
Summary: In this paper, a pseudo-partial likelihood estimation method is proposed to estimate parameters in the Cox proportional hazards model with right-censored and biased sampling data by adjusting sample risk sets. The asymptotic properties of the resulting estimator are studied, and a simulation study is conducted to illustrate the finite sample performance. The proposed method is also applied to analyze a set of HIV/AIDS data.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2024)
Article
Statistics & Probability
Liya Fu, Shuwen Hu, Jiaqi Li
Summary: Empirical likelihood (EL) is an effective nonparametric method that combines estimating equations flexibly and adaptively. A penalized EL method based on robust estimating functions is proposed for variable selection in a high-dimensional model, allowing the dimensions to grow exponentially with the sample size. The proposed method improves robustness and effectiveness in the presence of outliers or heavy-tailed data. Extensive simulation studies and a real data example demonstrate the enhanced variable selection accuracy when dealing with heavy-tailed data or outliers.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2024)
Article
Statistics & Probability
Yifan Sun, Ziyi Liu, Wu Wang
Summary: This paper extends the classical functional linear regression model to allow for heterogeneous coefficient functions among different subgroups of subjects. A penalization-based approach is proposed to simultaneously determine the number and structure of subgroups and coefficient functions within each subgroup. The paper provides an effective computational algorithm and establishes the oracle properties and estimation consistency of the model. Extensive numerical simulations demonstrate its superiority compared to competing methods, and an analysis of an air quality dataset leads to interesting findings and improved predictions.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2024)
Article
Statistics & Probability
Takemi Yanagimoto, Yoichi Miyata
Summary: A Bayesian estimator is proposed to improve the conditional maximum likelihood estimation by introducing a pair of priors. The conditional maximum likelihood estimation is explained using the posterior mode under a prior, and a promising estimator is defined using the posterior mean under a corresponding prior. The advantages of this approach include two different optimality properties of the induced estimator, the ease of various extensions, and the possible treatments for a finite sample size. The existing approaches are discussed and critiqued.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2024)
Article
Statistics & Probability
Sameera Hewage, Yongli Sang
Summary: This paper introduces a new method for measuring dependence, the categorical Gini correlation rho(g), and proposes a Jackknife empirical likelihood approach for constructing confidence intervals. Simulation studies and real data applications demonstrate competitive performance of the proposed method in terms of coverage accuracy and interval length.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2024)
Article
Statistics & Probability
Isadora Antoniano-Villalobos, Cristiano Villa, Stephen G. Walker
Summary: Constructing objective priors for multidimensional parameter spaces is challenging, and a common approach assumes independence and uses standard objective methods to obtain marginal distributions. In this paper, a novel objective prior is proposed by extending the objective method for one-dimensional case, allowing for a dependence structure in multidimensional parameter spaces.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2024)
Article
Statistics & Probability
Hui Li, Liuqing Yang, Kashinath Chatterjee, Min-Qian Liu
Summary: Supersaturated design (SSD) plays a crucial role in factor screening, and E(f(NOD)) criterion is one of the most widely used criteria for evaluating multi-level and mixed-level SSDs. This paper provides methods to construct multi-level E(f(NOD)) optimal SSDs with general run sizes, which can also be extended to mixed-level SSDs. The main idea of these methods is to combine two processed generalized Hadamard matrices with the expansive replacement method. These proposed methods are easy to implement, and the non-orthogonality between any two columns of the resulting SSDs is well controlled by that of the source designs.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2024)
Article
Statistics & Probability
Victoria L. Leaver, Robert G. Clark, Pavel N. Krivitsky, Carole L. Birrell
Summary: This article compares three likelihood approaches to estimation under informative sampling and examines their efficiency and asymptotic variance. The study shows that sample likelihood estimation approaches the efficiency of full maximum likelihood estimation when the sample size tends to infinity and the sampling fraction tends to zero. However, when the sample size tends to infinity and the sampling fraction is not negligible, maximum likelihood estimation is more efficient due to considering the possibility of duplicate samples. Pseudo-likelihood estimation can perform poorly in certain cases. For a special case where the superpopulation is exponential and the selection is probability proportional to size, the anticipated variance of pseudo-likelihood estimation is infinite.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2024)
Article
Statistics & Probability
Fadoua Balabdaoui, Harald Besdziek
Summary: The two-component mixture model with known background density, unknown signal density, and unknown mixing proportion has been studied in this paper. The log-concave MLE of the signal density is computed using the estimator of Patra & Sen (2016), and its consistency and convergence are shown. The performance of this method is evaluated through a simulation study.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2024)
Article
Statistics & Probability
V. Girardin, R. Senoussi
Summary: This paper investigates different issues related to stationarity reduction in autoregressive models, including both continuous and discrete time cases. Necessary and sufficient conditions for autoregressive models to be weakly stationary are explored, with explicit formulas for the time changes. Furthermore, the issue of stationarity reduction for discrete sequences sampled from continuous time autoregressive processes is also considered.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2024)
Article
Statistics & Probability
Juan Jose Fernandez-Duran, Maria Mercedes Gregorio-Dominguez
Summary: This paper presents the application of nonnegative trigonometric sums (NNTS) models in circular data analysis. Regression models for circular-dependent variables are constructed by fitting great circles on the parameter hypersphere, enabling the identification of different regions along the circle. The transformation of the original circular variable into a linear variable allows for the application of common linear regression methods in circular data analysis.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2024)
Article
Statistics & Probability
Miao Han, Yuanyuan Lin, Wenxin Liu, Zhanfeng Wang
Summary: The article proposes a method based on maximum rank correlation and concave fusion to automatically determine the number of subgroups, identify subgroup structure, and estimate subgroup-specific covariate effects. The method can be used without prior grouping information and is applicable to handling censored data.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2024)
Article
Statistics & Probability
Qing He, Hsin-Hsiung Huang
Summary: This article introduces a method for spatiotemporal data analysis with massive zeros, which is widely used in epidemiology and public health. The method fits zero-inflated negative binomial models using a Bayesian framework and employs latent variables from Polya-Gamma distributions to improve computational efficiency.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2024)